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Deep learning has become increasingly popular in both supervised and unsupervised machine learning thanks to its outstanding empirical performance.
Classification and regression trees
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Yoav Benjamini and Yosef Hochberg · 1995
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Conjugated linoleic acid reduces body fat mass in overweight and obese humans
Henrietta Blankson, Jacob A Stakkestad, Hans Fagertun, Erling Thom, Jan Wadstein, and Ola Gudmundsen · 2000
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The control of the false discovery rate in multiple testing under dependency
Yoav Benjamini and Daniel Yekutieli · 2001
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Leo Breiman · 2001
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Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach
John D. Storey, Jonathan E. Taylor, and David Siegmund · 2004
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Genotypic predictors of human immunodeficiency virus type 1 drug resistance
Soo-Yon Rhee, Jonathan Taylor, Gauhar Wadhera, Asa Ben-Hur, Douglas L Brutlag, and Robert W Shafer · 2006
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Correlation and large-scale simultaneous significance testing
Bradley Efron · 2007
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To how many simultaneous hypothesis tests can normal, student’s t t or bootstrap calibration be applied?
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Yin-Wen Chang and Chih-Jen Lin · 2008
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Wei Biao Wu · 2008
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Robustness of multiple testing procedures against dependence
Sandy Clarke and Peter Hall · 2009
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Strong approximations of level exceedences related to multiple hypothesis testing
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Gain weight by "going diet?" Artificial sweeteners and the neurobiology of sugar cravings: Neuroscience 2010
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Testing and detecting jumps based on a discretely observed process
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Fast and accurate approximation to significance tests in genome-wide association studies
Yu Zhang and Jun S. Liu · 2011
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Control of the false discovery rate under arbitrary covariance dependence (with discussion)
Jianqing Fan, Han Xu, and Weijie Gu · 2012
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Serum omega-6 polyunsaturated fatty acids and the metabolic syndrome: a longitudinal population-based cohort study
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Yoon Jung Yang, You Jin Kim, Yoon Kyoung Yang, Ji Yeon Kim, and Oran Kwon · 2012
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The mythos of model interpretability
Zachary C Lipton · 2016
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Predicting the future - big data, machine learning, and clinical medicine
Ziad Obermeyer and Ezekiel J Emanuel · 2016
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Dietary protein intake is associated with body mass index and weight up to 5 y of age in a prospective cohort of twins
Laura Pimpin, Susan Jebb, Laura Johnson, Jane Wardle, and Gina L Ambrosini · 2016
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High fat diet drives obesity regardless the composition of gut microbiota in mice
Sylvie Rabot, Mathieu Membrez, Florence Blancher, Bernard Berger, Déborah Moine, Lutz Krause, Rodrigo Bibiloni, Aurélia Bruneau, Philippe Gérard, Jay Siddharth, et al · 2016
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Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Variable selection for sparse dirichlet-multinomial regression with an application to microbiome data analysis
Jun Chen and Hongzhe Li · 2013
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Metabolic and structural effects of phosphatidylcholine and deoxycholate injections on subcutaneous fat: a randomized, controlled trial
Dominic N Reeds, B Selma Mohammed, Samuel Klein, Craig Brian Boswell, and V Leroy Young · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Systematic analysis of the association between gut flora and obesity through high-throughput sequencing and bioinformatics approaches
Chih-Min Chiu, Wei-Chih Huang, Shun-Long Weng, Han-Chi Tseng, Chao Liang, Wei-Chi Wang, Ting Yang, Tzu-Ling Yang, Chen-Tsung Weng, Tzu-Hao Chang, et al · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Variable selection in regression with compositional covariates
Wei Lin, Pixu Shi, Rui Feng, and Hongzhe Li · 2014
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Controlling the false discovery rate via knockoffs
Rina Foygel Barber and Emmanuel J Candès · 2015
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RANK: large-scale inference with graphical nonlinear knockoffs
Yingying Fan, Emre Demirkaya, Gaorong Li, and Jinchi Lv · 2017
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Nonuniformity of p-values can occur early in diverging dimensions
Yingying Fan, Emre Demirkaya, and Jinchi Lv · 2017
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Interpretation of neural networks is fragile
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Connections between the human gut microbiome and gestational diabetes mellitus
Ya-Shu Kuang, Jin-Hua Lu, Sheng-Hui Li, Jun-Hua Li, Ming-Yang Yuan, Jian-Rong He, Nian-Nian Chen, Wan-Qing Xiao, Song-Ying Shen, Lan Qiu, et al · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Comparative analysis of gut microbiota associated with body mass index in a large korean cohort
Yeojun Yun, Han-Na Kim, Song E Kim, Seong Gu Heo, Yoosoo Chang, Seungho Ryu, Hocheol Shin, and Hyung-Lae Kim · 2017
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Nonsparse learning with latent variables
Zemin Zheng, Jinchi Lv, and Wei Lin · 2017
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Panning for gold: Model-X knockoffs for high-dimensional controlled variable selection
Emmanuel J Candès, Yingying Fan, Lucas Janson, and Jinchi Lv · 2018
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